{"id":"W7034631691","doi":"","title":"Validation of log logistic distribution to model water demand using UK and North American data","year":2016,"lang":"en","type":"other","venue":"Greenwich Academic Literature Archive (University of Greenwich)","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Log-logistic distribution; Statistic; Probability distribution; Probability density function; Goodness of fit; Consumption (sociology); Water consumption","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002695702,0.0003141141,0.0005557295,0.0001399431,0.0001084598,0.00001047006,0.0008878511,0.0002477256,0.0002958292],"category_scores_gemma":[0.00002784422,0.0002636506,0.00006419284,0.0002466441,0.0006738605,0.0002119844,0.001206158,0.0004039001,0.00004948961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008670335,"about_ca_system_score_gemma":0.00002666428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0023,"about_ca_topic_score_gemma":0.003977825,"domain_scores_codex":[0.9981963,0.000136338,0.0002766938,0.0006804155,0.0003610427,0.0003492218],"domain_scores_gemma":[0.9984782,0.00007271364,0.0005020021,0.0007151566,0.00002222406,0.0002097433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001124474,0.0002643835,0.5091878,0.002282348,0.001156041,0.0001341567,0.0143888,0.008902104,0.01623361,0.003968433,0.4189611,0.0233967],"study_design_scores_gemma":[0.006388039,0.001595505,0.08962935,0.00919023,0.003319555,0.0002643311,0.001469502,0.6478406,0.0004446986,0.01618695,0.2168535,0.006817668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2566901,0.0002770679,0.6953093,0.000313441,0.0001045715,0.001107259,0.03229704,0.00007084996,0.0138303],"genre_scores_gemma":[0.9399624,0.000652335,0.01746001,0.0001005638,0.0001975268,0.000001014208,0.008971022,0.0001747834,0.03248036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6832723,"threshold_uncertainty_score":0.9999816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0228131834982691,"score_gpt":0.2336869628483548,"score_spread":0.2108737793500857,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}